{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "38c209d0",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "61bb02eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.linear_model import LinearRegression, LogisticRegression\n",
    "from sklearn.metrics import mean_squared_error, accuracy_score\n",
    "import statsmodels.api as sm\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b3610f6b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置随机种子\n",
    "np.random.seed(42)\n",
    "\n",
    "# 生成合成数据\n",
    "n = 1000\n",
    "study_hours = np.random.normal(5, 2, n)  # 学习时间\n",
    "tutoring = (study_hours + np.random.normal(0, 1, n) > 5).astype(int)  # 补习与学习时间相关\n",
    "score = 60 + 5 * study_hours + 10 * tutoring + np.random.normal(0, 5, n)  # 成绩\n",
    "pass_exam = (score > 70).astype(int)  # 是否通过考试\n",
    "\n",
    "# 创建DataFrame\n",
    "data = pd.DataFrame({\n",
    "    'study_hours': study_hours,\n",
    "    'tutoring': tutoring,\n",
    "    'score': score,\n",
    "    'pass_exam': pass_exam\n",
    "})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4bddb38d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Prediction MSE: 24.14\n",
      "Coefficients: [5.08830838 9.81989178]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 准备数据\n",
    "X = data[['study_hours', 'tutoring']]\n",
    "y = data['score']\n",
    "\n",
    "# 训练线性回归模型\n",
    "model_pred = LinearRegression()\n",
    "model_pred.fit(X, y)\n",
    "\n",
    "# 预测\n",
    "y_pred = model_pred.predict(X)\n",
    "\n",
    "# 评估\n",
    "mse = mean_squared_error(y, y_pred)\n",
    "print(f\"Prediction MSE: {mse:.2f}\")\n",
    "print(f\"Coefficients: {model_pred.coef_}\")\n",
    "\n",
    "# 可视化\n",
    "plt.scatter(data['study_hours'], y, label='True Score', alpha=0.5)\n",
    "plt.scatter(data['study_hours'], y_pred, label='Predicted Score', alpha=0.5)\n",
    "plt.xlabel('Study Hours')\n",
    "plt.ylabel('Score')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c250d582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Accuracy: 0.94\n",
      "Coefficients: [[1.97033644 0.80615848]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 准备数据\n",
    "y_class = data['pass_exam']\n",
    "\n",
    "# 训练逻辑回归模型\n",
    "model_class = LogisticRegression()\n",
    "model_class.fit(X, y_class)\n",
    "\n",
    "# 预测\n",
    "y_class_pred = model_class.predict(X)\n",
    "\n",
    "# 评估\n",
    "accuracy = accuracy_score(y_class, y_class_pred)\n",
    "print(f\"Classification Accuracy: {accuracy:.2f}\")\n",
    "print(f\"Coefficients: {model_class.coef_}\")\n",
    "\n",
    "# 可视化\n",
    "plt.scatter(data['study_hours'], y_class, label='True Pass', alpha=0.5)\n",
    "plt.scatter(data['study_hours'], y_class_pred, label='Predicted Pass', alpha=0.5)\n",
    "plt.xlabel('Study Hours')\n",
    "plt.ylabel('Pass (0/1)')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9ff33f2d",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of [Index(['propensity_score'], dtype='object')] are in the [columns]\"",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyError\u001b[39m                                  Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[5]\u001b[39m\u001b[32m, line 15\u001b[39m\n\u001b[32m     13\u001b[39m \u001b[38;5;66;03m# 使用最近邻匹配\u001b[39;00m\n\u001b[32m     14\u001b[39m nn = NearestNeighbors(n_neighbors=\u001b[32m1\u001b[39m)\n\u001b[32m---> \u001b[39m\u001b[32m15\u001b[39m nn.fit(\u001b[43mcontrol\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[33;43m'\u001b[39;49m\u001b[33;43mpropensity_score\u001b[39;49m\u001b[33;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m.values)\n\u001b[32m     16\u001b[39m distances, indices = nn.kneighbors(treated[[\u001b[33m'\u001b[39m\u001b[33mpropensity_score\u001b[39m\u001b[33m'\u001b[39m]].values)\n\u001b[32m     18\u001b[39m \u001b[38;5;66;03m# 计算平均处理效应（ATE）\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mg:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\pandas\\core\\frame.py:4108\u001b[39m, in \u001b[36mDataFrame.__getitem__\u001b[39m\u001b[34m(self, key)\u001b[39m\n\u001b[32m   4106\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m is_iterator(key):\n\u001b[32m   4107\u001b[39m         key = \u001b[38;5;28mlist\u001b[39m(key)\n\u001b[32m-> \u001b[39m\u001b[32m4108\u001b[39m     indexer = \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43mcolumns\u001b[49m\u001b[43m.\u001b[49m\u001b[43m_get_indexer_strict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[33;43m\"\u001b[39;49m\u001b[33;43mcolumns\u001b[39;49m\u001b[33;43m\"\u001b[39;49m\u001b[43m)\u001b[49m[\u001b[32m1\u001b[39m]\n\u001b[32m   4110\u001b[39m \u001b[38;5;66;03m# take() does not accept boolean indexers\u001b[39;00m\n\u001b[32m   4111\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mgetattr\u001b[39m(indexer, \u001b[33m\"\u001b[39m\u001b[33mdtype\u001b[39m\u001b[33m\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) == \u001b[38;5;28mbool\u001b[39m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32mg:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6200\u001b[39m, in \u001b[36mIndex._get_indexer_strict\u001b[39m\u001b[34m(self, key, axis_name)\u001b[39m\n\u001b[32m   6197\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m   6198\u001b[39m     keyarr, indexer, new_indexer = \u001b[38;5;28mself\u001b[39m._reindex_non_unique(keyarr)\n\u001b[32m-> \u001b[39m\u001b[32m6200\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m.\u001b[49m\u001b[43m_raise_if_missing\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkeyarr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   6202\u001b[39m keyarr = \u001b[38;5;28mself\u001b[39m.take(indexer)\n\u001b[32m   6203\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, Index):\n\u001b[32m   6204\u001b[39m     \u001b[38;5;66;03m# GH 42790 - Preserve name from an Index\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32mg:\\TempTest\\TempDownload\\my_env\\Lib\\site-packages\\pandas\\core\\indexes\\base.py:6249\u001b[39m, in \u001b[36mIndex._raise_if_missing\u001b[39m\u001b[34m(self, key, indexer, axis_name)\u001b[39m\n\u001b[32m   6247\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m nmissing:\n\u001b[32m   6248\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m nmissing == \u001b[38;5;28mlen\u001b[39m(indexer):\n\u001b[32m-> \u001b[39m\u001b[32m6249\u001b[39m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mNone of [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mkey\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m] are in the [\u001b[39m\u001b[38;5;132;01m{\u001b[39;00maxis_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m]\u001b[39m\u001b[33m\"\u001b[39m)\n\u001b[32m   6251\u001b[39m     not_found = \u001b[38;5;28mlist\u001b[39m(ensure_index(key)[missing_mask.nonzero()[\u001b[32m0\u001b[39m]].unique())\n\u001b[32m   6252\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mnot_found\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m not in index\u001b[39m\u001b[33m\"\u001b[39m)\n",
      "\u001b[31mKeyError\u001b[39m: \"None of [Index(['propensity_score'], dtype='object')] are in the [columns]\""
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import NearestNeighbors\n",
    "\n",
    "# 计算倾向评分\n",
    "propensity_model = LogisticRegression()\n",
    "propensity_model.fit(data[['study_hours']], data['tutoring'])\n",
    "propensity_scores = propensity_model.predict_proba(data[['study_hours']])[:, 1]\n",
    "\n",
    "# 匹配处理组和对照组\n",
    "treated = data[data['tutoring'] == 1]\n",
    "control = data[data['tutoring'] == 0]\n",
    "\n",
    "# 使用最近邻匹配\n",
    "nn = NearestNeighbors(n_neighbors=1)\n",
    "nn.fit(control[['propensity_score']].values)\n",
    "distances, indices = nn.kneighbors(treated[['propensity_score']].values)\n",
    "\n",
    "# 计算平均处理效应（ATE）\n",
    "treated_scores = treated['score'].values\n",
    "control_scores = control.iloc[indices.flatten()]['score'].values\n",
    "ate = np.mean(treated_scores - control_scores)\n",
    "print(f\"Estimated Average Treatment Effect (ATE): {ate:.2f}\")\n",
    "\n",
    "# 可视化倾向评分分布\n",
    "plt.hist(propensity_scores[data['tutoring'] == 1], bins=20, alpha=0.5, label='Treated')\n",
    "plt.hist(propensity_scores[data['tutoring'] == 0], bins=20, alpha=0.5, label='Control')\n",
    "plt.xlabel('Propensity Score')\n",
    "plt.ylabel('Frequency')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
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